CN114091773A - System life prediction method, device, electronic equipment and storage medium - Google Patents

System life prediction method, device, electronic equipment and storage medium Download PDF

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CN114091773A
CN114091773A CN202111422339.7A CN202111422339A CN114091773A CN 114091773 A CN114091773 A CN 114091773A CN 202111422339 A CN202111422339 A CN 202111422339A CN 114091773 A CN114091773 A CN 114091773A
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谭启涛
谭董
王开业
孙广栋
范波
敬龙儿
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Chengdu Aerospace Science And Industry Big Data Research Institute Co ltd
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Abstract

The application provides a system life prediction method and device, electronic equipment and a storage medium, and relates to the technical field of life prediction. The method comprises the steps of establishing a joint density function of the working lives of a plurality of first components, determining a life distribution function of a system to be predicted, estimating the working life distribution function corresponding to each component, selecting an alternative correlation function based on a scatter diagram of the working life distribution functions corresponding to any two components, determining a likelihood function of each alternative correlation function, estimating an estimated value of a parameter carried in each alternative correlation function, calculating a theoretical value and an estimated value of each alternative correlation function, selecting a target correlation function according to the theoretical value and the estimated value, and estimating the predicted life of the system to be predicted according to the target correlation function and the life distribution function of the system to be predicted. The method, the device, the electronic equipment and the storage medium can accurately predict the system life of the system.

Description

System life prediction method, device, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of life prediction technologies, and in particular, to a method and an apparatus for predicting system life, an electronic device, and a storage medium.
Background
For some core component products, such as core components of machine tools and weaponry, due to the complex manufacture and high cost, once the equipment is out of order, the shutdown and production halt will result in huge economic loss. Due to the importance, if the service life of a system consisting of core parts and backup components can be predicted, the method has great significance for predictive maintenance, timely replacement and the like of the system.
At present, the common method for predicting the service life is to fit a degradation track through a linear curve and a polynomial curve, however, the service life can be predicted only for some independent parts by adopting the method, and the service life cannot be predicted for a system consisting of a plurality of parts.
Therefore, how to provide an effective solution to accurately predict the system lifetime of a system composed of multiple components has become an urgent problem in the prior art.
Disclosure of Invention
In a first aspect, an embodiment of the present application provides a system life prediction method, including:
establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
determining a service life distribution function of the system to be predicted based on the joint density function of the working lives of the plurality of first components;
observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components;
estimating a working life distribution function corresponding to each of the plurality of second components based on the working life matrixes of the plurality of second components;
traversing a scatter diagram of the working life distribution functions corresponding to any two parts in the second parts, and selecting a plurality of correlation functions corresponding to the working life distribution functions which accord with response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
determining a likelihood function for each alternative correlation function;
estimating the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
estimating the estimation value of each alternative correlation function according to the frequency estimation probability;
selecting the alternative correlation function with the smallest sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
In one possible design, the method further includes:
and determining the residual life of the system to be predicted at any time according to the predicted life of the system to be predicted.
In one possible design, the joint density function of the operational life of the plurality of first components is
Figure BDA0003377837240000021
Wherein
Figure BDA0003377837240000022
Density function, H (x), representing the working life of the ith first component1,x2,…,xn) A joint distribution function representing the working life of a plurality of first components, Ct(u1,u2,…,un) A correlation function, u, representing the operational life of a plurality of first componentsnA variable in the correlation function representing the operational lifetime of the nth first component, n being a positive integer greater than 1,
Figure BDA0003377837240000023
a derivative of a joint distribution function representing the operational life of the plurality of first components,
Figure BDA0003377837240000024
derivative of a correlation function, x, representing the operational life of a plurality of first componentsnIndicating the working life of the nth first component,
Figure BDA0003377837240000031
the derivative of the working life of the nth first component is represented.
In one possible design, the life distribution function of the system to be predicted is
Figure BDA0003377837240000032
Wherein P (X)sX) represents a distribution function of the system life to be predicted which is less than or equal to X, XnRepresenting the working life of the nth first component, x representing a time variable, Ct(u1,u2,…,un) A correlation function, u, representing the operational life of a plurality of first componentsnA variable in the correlation function representing the operational lifetime of the nth first component, n being a positive integer greater than 1,
Figure BDA0003377837240000033
a derivative of a correlation function representing the operational life of the plurality of first components,
Figure BDA0003377837240000034
a derivative of a variable in the correlation function representing the operational life of the nth first component.
In one possible design, the likelihood function of the alternative correlation function is
Figure BDA0003377837240000035
Wherein
Figure BDA0003377837240000036
Representing the derivative of the corresponding operating life distribution function at the jth observation of the ith second component,
Figure BDA0003377837240000037
a correlation function, β, representing the working life of the second componenttA parameter carried by a correlation function representing the operating life of the second component, n representing the number of second components, m representing the number of observations of the operating life of each second component,
Figure BDA0003377837240000038
and the parameters represent the parameters of the corresponding working life density function of the ith second component during the jth observation, and the corresponding working life density function of the ith second component during the jth observation is the derivative of the corresponding working life distribution function of the ith second component during the jth observation.
In one possible design, the theoretical value of the alternative correlation function is
Figure BDA0003377837240000039
Wherein P (X)1≤x1 (l),X2≤x2 (l),…,Xn≤xn (l)) Represents X1≤x1 (l),X2≤x2 (l),…,Xn≤xn (l)Probability of (A), XnRepresenting the nth second partWorking life, xn (l)Indicating the ith life data in the nth column when each column of life data in the working life matrix is arranged in a small-to-square manner, m indicating the number of observations of the working life of each second component,
Figure BDA0003377837240000041
an expected value of a parameter carried by a correlation function representing the operational life of the second component.
In one possible design, the estimated value of the alternative correlation function is
Figure BDA0003377837240000042
Wherein xi,nAnd representing the service life data of the ith row and the nth column in the service life matrix.
In a second aspect, an embodiment of the present application provides a system life prediction apparatus, including:
the system comprises an establishing unit, a calculating unit and a predicting unit, wherein the establishing unit is used for establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
a first determining unit, configured to determine a life distribution function of the system to be predicted based on a joint density function of the working lives of the plurality of first components;
the second determining unit is used for observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components and are of the same type;
the first arithmetic unit is used for estimating a working life distribution function corresponding to each part in the plurality of second parts based on the working life matrixes of the plurality of second parts;
the first selection unit is used for traversing a scatter diagram of the working life distribution functions corresponding to any two of the second components, and selecting a plurality of correlation functions corresponding to the working life distribution functions which meet response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
a third determining unit for determining a likelihood function for each alternative correlation function;
the second operation unit estimates the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
the third operation unit is used for calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
the fourth arithmetic unit estimates the estimation value of each alternative correlation function according to the frequency estimation probability;
the second selection unit is used for selecting the alternative correlation function with the minimum sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and the fifth arithmetic unit is used for estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete mutual communication through the bus;
a memory for storing a computer program;
the processor is used for executing the program stored in the memory and realizing the following processes:
establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
determining a service life distribution function of the system to be predicted based on the joint density function of the working lives of the plurality of first components;
observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components;
estimating a working life distribution function corresponding to each of the plurality of second components based on the working life matrixes of the plurality of second components;
traversing a scatter diagram of the working life distribution functions corresponding to any two parts in the second parts, and selecting a plurality of correlation functions corresponding to the working life distribution functions which accord with response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
determining a likelihood function for each alternative correlation function;
estimating the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
estimating the estimation value of each alternative correlation function according to the frequency estimation probability;
selecting the alternative correlation function with the smallest sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored in the storage medium, and when the computer program is executed by a processor, the computer program implements the following procedures:
establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
determining a service life distribution function of the system to be predicted based on the joint density function of the working lives of the plurality of first components;
observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components;
estimating a working life distribution function corresponding to each of the plurality of second components based on the working life matrixes of the plurality of second components;
traversing a scatter diagram of the working life distribution functions corresponding to any two parts in the second parts, and selecting a plurality of correlation functions corresponding to the working life distribution functions which accord with response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
determining a likelihood function for each alternative correlation function;
estimating the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
estimating the estimation value of each alternative correlation function according to the frequency estimation probability;
selecting the alternative correlation function with the smallest sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
The above-mentioned at least one technical scheme that this application one or more embodiments adopted can reach following beneficial effect:
the system life prediction scheme provided by the embodiment of the application determines the life distribution function of the system to be predicted through the combined density function of the working lives of a plurality of first components in the system to be predicted, estimates the working life distribution function corresponding to each component based on the working life matrixes of a plurality of second components, selecting an alternative correlation function based on a scatter diagram of the working life distribution function corresponding to any two components, determining a likelihood function of each alternative correlation function, estimating an estimation value of a parameter carried in each alternative correlation function, and finally, estimating the predicted service life of the system to be predicted according to the target correlation function and the service life distribution function of the system to be predicted. Therefore, the system service life of the system comprising a plurality of components can be accurately predicted, decision basis is provided for predictive maintenance and replacement of the system, and huge economic loss caused by shutdown and production halt due to faults of the system is avoided.
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The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure in any way. In the drawings:
fig. 1 is a flowchart of a system life prediction method according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a system life prediction apparatus according to an embodiment of the present application.
Detailed Description
In order to accurately predict the service life of a system composed of a plurality of components, embodiments of the present application provide a method, an apparatus, an electronic device, and a storage medium for predicting the service life of a system composed of a plurality of components.
The product system prediction method provided by the embodiment of the application can be applied to a user terminal, and the user terminal can be, but is not limited to, a personal computer, a smart phone, a tablet computer, a Personal Digital Assistant (PDA), and the like.
The system life prediction method provided by the embodiment of the present application will be described in detail below.
For convenience of description, the embodiments of the present application are described with reference to a user terminal as an implementation subject, unless otherwise specified.
It is to be understood that the described execution body does not constitute a limitation of the embodiments of the present application.
As shown in fig. 1, a system life prediction method provided in an embodiment of the present application may include the following steps:
step S101, establishing a joint density function of the working lives of a plurality of first components in the system to be predicted.
In the embodiment of the application, the plurality of first components are all components in the system to be predicted, the working lives of the plurality of first components are not independent of each other, and the life of the system to be predicted relates to a life joint density function of each first component. Therefore, when predicting the service life of the system, a joint density function of the working life of a plurality of first components in the system to be predicted can be established.
The joint density function of the operational life of the plurality of first components may be expressed as
Figure BDA0003377837240000081
Wherein
Figure BDA0003377837240000082
A density function representing the operational lifetime of the ith first component,
Figure BDA0003377837240000083
a function representing the distribution of the working life of the ith first component, a function representing the density of the working life of the first component
Figure BDA0003377837240000084
As a function of the operating life distribution of the first component
Figure BDA0003377837240000085
Derivative of (a), H (x)1,x2,…,xn) A joint distribution function representing the working life of a plurality of first components, Ct(u1,u2,…,un) A correlation function, x, representing the working life of the first componentnDenotes the working life, u, of the nth first componentnA variable in the correlation function representing the operational lifetime of the nth first component, n being a positive integer greater than 1,
Figure BDA0003377837240000091
a derivative of a joint distribution function representing the operational life of the plurality of first components,
Figure BDA0003377837240000092
a derivative of a correlation function representing the operational life of the plurality of first components,
Figure BDA0003377837240000093
the derivative of the working life of the nth first component is represented.
Step S102, determining a service life distribution function of the system to be predicted based on the combined density function of the working lives of the plurality of first components.
In the embodiment of the present application, the life distribution function of the system to be predicted may be expressed as
Figure BDA0003377837240000094
Wherein P (X)sX) represents a distribution function of the system life to be predicted which is less than or equal to X, XnRepresenting the working life of the nth first component, x representing a time variable, Ct(u1,u2,…,un) A correlation function, u, representing the operational life of a plurality of first componentsnA variable in the correlation function representing the operational lifetime of the nth first component, n being a positive integer greater than 1,
Figure BDA0003377837240000095
a derivative of a correlation function representing the operational life of the plurality of first components,
Figure BDA0003377837240000096
a derivative of a variable in the correlation function representing the operational life of the nth first component.
And step S103, observing the working lives of the second components for multiple times to obtain a working life matrix of the second components.
The plurality of second components are in one-to-one correspondence with the plurality of first components, or the plurality of second components are in one-to-one correspondence with the plurality of first components.
For example, in one embodiment, the first plurality of components includes components a1, a2, and A3 and the second plurality of components includes components B1, B2, and B3, wherein components a1 and B1 may be the same component or the same type of component, components a2 and B2 may be the same component or the same type of component, and components A3 and B3 may be the same component or the same type of component.
Assuming that the number of the second members is n, and the working life of each second member is observed m times, the obtained working life is xijI is an integer from 1 to n, and j is an integer from 1 to m, and represents the working life observed when the ith second component is observed for the jth time. The operational life matrix of the plurality of second components may be represented as
Figure BDA0003377837240000101
n and m are both positive integers greater than 1.
And step S104, estimating the service life distribution function corresponding to each part in the second parts based on the service life matrixes of the second parts.
In the embodiment of the application, the working life of each second component is observed m times, and m working lives can be observed for each second component, so that an m × n (n second components) working life matrix is obtained, and each column of data of the working life matrix corresponds to m working lives of one second component. Therefore, a plurality of data can be estimated according to each row in the working life matrixThe working life distribution function corresponding to each component in the second component can be expressed as
Figure BDA0003377837240000102
Step S105, traversing a scatter diagram of the working life distribution functions corresponding to any two of the second components, and selecting a plurality of correlation functions corresponding to the working life distribution functions which accord with the response characteristics as alternative correlation functions.
In the embodiment of the present application, after the working life distribution functions corresponding to each of the plurality of second components are estimated, a scatter diagram of the working life distribution functions corresponding to any two of the plurality of second components may be traversed to determine the working life distribution functions that meet the response characteristics, and then a plurality of (second component) working life correlation functions corresponding to the determined working life distribution functions are selected as the alternative correlation functions. The correlation function corresponding to the service life distribution function means that the response characteristics of the correlation function and the service life distribution function are consistent. The response characteristic may be, but is not limited to, a linear characteristic, a Clayton Copula distribution characteristic, a Frank Copula distribution characteristic, and the like.
For example, in one embodiment, the system includes the second component A, B, C, D, and the scatter diagram of the operating life distribution function corresponding to the second component A, B, the scatter diagram of the operating life distribution function corresponding to the second component A, C, the scatter diagram of the operating life distribution function corresponding to the second component A, D, the scatter diagram of the operating life distribution function corresponding to the second component B, C, the scatter diagram of the operating life distribution function corresponding to the second component B, D, and the scatter diagram of the operating life distribution function corresponding to the second component C, D may be observed during observation.
In step S106, a likelihood function for each alternative correlation function is determined.
Specifically, the likelihood function of the alternative correlation function may be expressed as
Figure BDA0003377837240000111
Wherein
Figure BDA0003377837240000112
Representing the derivative of the corresponding service life distribution function of the ith second component at the jth observation, k being an integer greater than 1,
Figure BDA0003377837240000113
a correlation function, β, representing the working life of the second componenttA parameter carried by a correlation function representing the operating life of the second component, n representing the number of second components, m representing the number of observations of the operating life of each second component,
Figure BDA0003377837240000114
and the parameters represent the parameters of the corresponding working life density function of the ith second component during the jth observation, and the corresponding working life density function of the ith second component during the jth observation is the derivative of the corresponding working life distribution function of the ith second component during the jth observation.
And step S107, estimating the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function.
And step S108, calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions.
Specifically, the data in the working life matrix of the second components can be sorted according to the column variables
Figure BDA0003377837240000115
And then calculating theoretical values of the alternative correlation functions according to the sorted working life matrix and the estimated values of the parameters carried in the alternative correlation functions. The theoretical value of the alternative correlation function may be expressed as
Figure BDA0003377837240000116
WhereinP(X1≤x1 (l),X2≤x2 (l),…,Xn≤xn (l)) Represents X1≤x1 (l),X2≤x2 (l),…,Xn≤xn (l)Probability of (A), XnDenotes the working life, x, of the nth second componentn (l)Indicating the ith life data in the nth column when each column of life data in the working life matrix is arranged in a small-to-square manner, m indicating the number of observations of the working life of each second component,
Figure BDA0003377837240000121
an expected value of a parameter carried by a correlation function representing the operational life of the second component.
Step S109, estimating the estimation value of each alternative correlation function according to the frequency estimation probability.
The estimate of the alternative correlation function may be expressed as
Figure BDA0003377837240000122
Wherein xi,nAnd representing the service life data of the ith row and the nth column in the service life matrix.
Step S110, according to the theoretical value and the estimated value of each alternative correlation function, selecting the alternative correlation function with the minimum sum of absolute errors of the theoretical value and the estimated value as the target correlation function.
Specifically, the sum of absolute errors of the theoretical value and the estimated value of each alternative correlation function is calculated for the theoretical value and the estimated value of each alternative correlation function, and then the alternative correlation function in which the sum of absolute errors of the theoretical value and the estimated value is the minimum is selected as the target correlation function.
And step S111, estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
Specifically, the target correlation function may be substituted into the life distribution function of the system to be predicted to obtain the life distribution function to be predictedAnd measuring the predicted service life of the system. The predicted life of the system to be predicted may be expressed as
Figure BDA0003377837240000123
Wherein f iss(t) is the life distribution function F of the system to be predicteds(t) derivative of (t).
Further, after the predicted service life of the system to be predicted is estimated, the remaining service life of the system to be predicted at any time can be determined according to the predicted service life of the system to be predicted. The remaining life of the system to be predicted at any time a can be expressed as
Figure BDA0003377837240000124
In summary, the system life prediction method provided in the embodiment of the present application determines the life distribution function of the system to be predicted through the joint density function of the working lives of the plurality of first components in the system to be predicted, estimates the working life distribution function corresponding to each component based on the working life matrix of the plurality of second components, selecting an alternative correlation function based on a scatter diagram of the working life distribution function corresponding to any two components, determining a likelihood function of each alternative correlation function, estimating an estimation value of a parameter carried in each alternative correlation function, and finally, estimating the predicted service life of the system to be predicted according to the target correlation function and the service life distribution function of the system to be predicted. Therefore, the system service life of the system comprising a plurality of components can be accurately predicted, decision basis is provided for predictive maintenance and replacement of the system, and huge economic loss caused by shutdown and production halt due to faults of the system is avoided.
Fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 2, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 2, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the system life prediction device on the logic level. The processor executes the program stored in the memory and realizes the following processes:
establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
determining a service life distribution function of the system to be predicted based on the joint density function of the working lives of the plurality of first components;
observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components;
estimating a working life distribution function corresponding to each of the plurality of second components based on the working life matrixes of the plurality of second components;
traversing a scatter diagram of the working life distribution functions corresponding to any two parts in the second parts, and selecting a plurality of correlation functions corresponding to the working life distribution functions which accord with response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
determining a likelihood function for each alternative correlation function;
estimating the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
estimating the estimation value of each alternative correlation function according to the frequency estimation probability;
selecting the alternative correlation function with the smallest sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
The method performed by the system life prediction device according to the embodiment shown in fig. 2 of the present application may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in one or more embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present application may be embodied directly in the hardware decoding processor, or in a combination of the hardware and software modules included in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device may also execute the method shown in fig. 1 and implement the functions of the system life prediction apparatus in the embodiment shown in fig. 2, which are not described herein again in this embodiment of the present application.
Of course, besides the software implementation, the electronic device of the present application does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
Embodiments of the present application also provide a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to perform the method of the embodiment shown in fig. 1, and are specifically configured to:
establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
determining a service life distribution function of the system to be predicted based on the joint density function of the working lives of the plurality of first components;
observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components;
estimating a working life distribution function corresponding to each of the plurality of second components based on the working life matrixes of the plurality of second components;
traversing a scatter diagram of the working life distribution functions corresponding to any two parts in the second parts, and selecting a plurality of correlation functions corresponding to the working life distribution functions which accord with response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
determining a likelihood function for each alternative correlation function;
estimating the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
estimating the estimation value of each alternative correlation function according to the frequency estimation probability;
selecting the alternative correlation function with the smallest sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
Fig. 3 is a schematic structural diagram of a system life prediction apparatus according to an embodiment of the present application. Referring to fig. 3, in one software implementation, the system life prediction apparatus includes:
the system comprises an establishing unit, a calculating unit and a predicting unit, wherein the establishing unit is used for establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
a first determining unit, configured to determine a life distribution function of the system to be predicted based on a joint density function of the working lives of the plurality of first components;
the second determining unit is used for observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components and are of the same type;
the first arithmetic unit is used for estimating a working life distribution function corresponding to each part in the plurality of second parts based on the working life matrixes of the plurality of second parts;
the first selection unit is used for traversing a scatter diagram of the working life distribution functions corresponding to any two of the second components, and selecting a plurality of correlation functions corresponding to the working life distribution functions which meet response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
a third determining unit for determining a likelihood function for each alternative correlation function;
the second operation unit estimates the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
the third operation unit is used for calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
the fourth arithmetic unit estimates the estimation value of each alternative correlation function according to the frequency estimation probability;
the second selection unit is used for selecting the alternative correlation function with the minimum sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and the fifth arithmetic unit is used for estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
In short, the above description is only a preferred embodiment of this document, and is not intended to limit the scope of protection of this document. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document shall be included in the protection scope of this document.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
All the embodiments in this document are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

Claims (10)

1. A method for predicting system life, comprising:
establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
determining a service life distribution function of the system to be predicted based on the joint density function of the working lives of the plurality of first components;
observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components;
estimating a working life distribution function corresponding to each of the plurality of second components based on the working life matrixes of the plurality of second components;
traversing a scatter diagram of the working life distribution functions corresponding to any two parts in the second parts, and selecting a plurality of correlation functions corresponding to the working life distribution functions which accord with response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
determining a likelihood function for each alternative correlation function;
estimating the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
estimating the estimation value of each alternative correlation function according to the frequency estimation probability;
selecting the alternative correlation function with the smallest sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
2. The method of claim 1, further comprising:
and determining the residual life of the system to be predicted at any time according to the predicted life of the system to be predicted.
3. The method of claim 1, wherein the joint density function of the operational life of the plurality of first components is
Figure FDA0003377837230000021
Wherein
Figure FDA0003377837230000022
Density function, H (x), representing the working life of the ith first component1,x2,…,xn) A joint distribution function representing the working life of a plurality of first components, Ct(u1,u2,…,un) A correlation function, u, representing the operational life of a plurality of first componentsnA variable in the correlation function representing the operational lifetime of the nth first component, n being a positive integer greater than 1,
Figure FDA0003377837230000023
a derivative of a joint distribution function representing the operational life of the plurality of first components,
Figure FDA0003377837230000024
derivative of a correlation function, x, representing the operational life of a plurality of first componentsnIndicating the working life of the nth first component,
Figure FDA0003377837230000025
the derivative of the working life of the nth first component is represented.
4. The method of claim 1, wherein the life distribution function of the system to be predicted is
Figure FDA0003377837230000026
Wherein P (X)sX) represents a distribution function of the system life to be predicted which is less than or equal to X, XnRepresenting the working life of the nth first component, x representing a time variable, Ct(u1,u2,…,un) A correlation function, u, representing the operational life of a plurality of first componentsnA variable in the correlation function representing the operational lifetime of the nth first component, n being a positive integer greater than 1,
Figure FDA0003377837230000027
a derivative of a correlation function representing the operational life of the plurality of first components,
Figure FDA0003377837230000028
a derivative of a variable in the correlation function representing the operational life of the nth first component.
5. The method of claim 1, wherein the likelihood function of the alternative correlation function is
Figure FDA0003377837230000029
Wherein
Figure FDA00033778372300000210
Representing the derivative of the corresponding operating life distribution function at the jth observation of the ith second component,
Figure FDA0003377837230000031
a correlation function, β, representing the working life of the second componenttA parameter carried by a correlation function representing the operating life of the second component, n representing the number of second components, m representing the number of observations of the operating life of each second component,
Figure FDA0003377837230000032
and the parameters represent the parameters of the corresponding working life density function of the ith second component during the jth observation, and the corresponding working life density function of the ith second component during the jth observation is the derivative of the corresponding working life distribution function of the ith second component during the jth observation.
6. The method of claim 1, wherein the theoretical value of the alternative correlation function is selected as
Figure FDA0003377837230000033
Wherein P (X)1≤x1 (l),X2≤x2 (l),…,Xn≤xn (l)) Represents X1≤x1 (l),X2≤x2 (l),…,Xn≤xn (l)Probability of (A), XnDenotes the working life, x, of the nth second componentn (l)Indicating the ith life data in the nth column when each column of life data in the working life matrix is arranged in a small-to-square manner, m indicating the number of observations of the working life of each second component,
Figure FDA0003377837230000034
an expected value of a parameter carried by a correlation function representing the operational life of the second component.
7. The method of claim 6, wherein the estimate of the alternative correlation function is
Figure FDA0003377837230000035
Wherein xi,nIndicating said working lifeLife data of ith row and nth column in the matrix.
8. A system life prediction apparatus, comprising:
the system comprises an establishing unit, a calculating unit and a predicting unit, wherein the establishing unit is used for establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
a first determining unit, configured to determine a life distribution function of the system to be predicted based on a joint density function of the working lives of the plurality of first components;
the second determining unit is used for observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components and are of the same type;
the first arithmetic unit is used for estimating a working life distribution function corresponding to each part in the plurality of second parts based on the working life matrixes of the plurality of second parts;
the first selection unit is used for traversing a scatter diagram of the working life distribution functions corresponding to any two of the second components, and selecting a plurality of correlation functions corresponding to the working life distribution functions which meet response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
a third determining unit for determining a likelihood function for each alternative correlation function;
the second operation unit estimates the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
the third operation unit is used for calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
the fourth arithmetic unit estimates the estimation value of each alternative correlation function according to the frequency estimation probability;
the second selection unit is used for selecting the alternative correlation function with the minimum sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and the fifth arithmetic unit is used for estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing the communication between the processor and the memory through the bus;
a memory for storing a computer program;
the processor is used for executing the program stored in the memory and realizing the following processes:
establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
determining a service life distribution function of the system to be predicted based on the joint density function of the working lives of the plurality of first components;
observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components;
estimating a working life distribution function corresponding to each of the plurality of second components based on the working life matrixes of the plurality of second components;
traversing a scatter diagram of the working life distribution functions corresponding to any two parts in the second parts, and selecting a plurality of correlation functions corresponding to the working life distribution functions which accord with response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
determining a likelihood function for each alternative correlation function;
estimating the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
estimating the estimation value of each alternative correlation function according to the frequency estimation probability;
selecting the alternative correlation function with the smallest sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
10. A computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the following procedure:
establishing a joint density function of the working lives of a plurality of first components in the system to be predicted;
determining a service life distribution function of the system to be predicted based on the joint density function of the working lives of the plurality of first components;
observing the working lives of a plurality of second components for a plurality of times to obtain a working life matrix of the plurality of second components, wherein the plurality of second components are in one-to-one correspondence with the plurality of first components or the plurality of second components are in one-to-one correspondence with the plurality of first components;
estimating a working life distribution function corresponding to each of the plurality of second components based on the working life matrixes of the plurality of second components;
traversing a scatter diagram of the working life distribution functions corresponding to any two parts in the second parts, and selecting a plurality of correlation functions corresponding to the working life distribution functions which accord with response characteristics as alternative correlation functions, wherein the response characteristics are linear characteristics, Clayton Copula distribution characteristics or Frank Copula distribution characteristics;
determining a likelihood function for each alternative correlation function;
estimating the estimated value of the parameter carried in each alternative correlation function through a maximum likelihood estimation method and the likelihood function of each alternative correlation function;
calculating theoretical values of the alternative correlation functions according to the working life matrix of each second component and the estimated values of the parameters carried in the alternative correlation functions;
estimating the estimation value of each alternative correlation function according to the frequency estimation probability;
selecting the alternative correlation function with the smallest sum of absolute errors of the theoretical value and the estimated value as a target correlation function according to the theoretical value and the estimated value of each alternative correlation function;
and estimating the predicted service life of the system to be predicted based on the target correlation function and the service life distribution function of the system to be predicted.
CN202111422339.7A 2021-11-26 2021-11-26 System life prediction method, device, electronic equipment and storage medium Pending CN114091773A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117423175A (en) * 2023-10-25 2024-01-19 深圳泰瑞谷科技有限公司 Vehicle diagnosis data display method and device, diagnosis instrument and medium

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